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# Object Detection
Object detection is a form of supervised learning where a model is trained to identify
and categorize objects within images. AutoTrain simplifies the process, enabling you to
train a state-of-the-art object detection model by simply uploading labeled example images.
## Preparing your data
To ensure your object detection model trains effectively, follow these guidelines for preparing your data:
### Organizing Images
Prepare a zip file containing your images and metadata.jsonl.
```
Archive.zip
├── 0001.png
├── 0002.png
├── 0003.png
├── .
├── .
├── .
└── metadata.jsonl
```
Example for `metadata.jsonl`:
```
{"file_name": "0001.png", "objects": {"bbox": [[302.0, 109.0, 73.0, 52.0]], "category": [0]}}
{"file_name": "0002.png", "objects": {"bbox": [[810.0, 100.0, 57.0, 28.0]], "category": [1]}}
{"file_name": "0003.png", "objects": {"bbox": [[160.0, 31.0, 248.0, 616.0], [741.0, 68.0, 202.0, 401.0]], "category": [2, 2]}}
```
Please note that bboxes need to be in COCO format `[x, y, width, height]`.
### Image Requirements
- Format: Ensure all images are in JPEG, JPG, or PNG format.
- Quantity: Include at least 5 images to provide the model with sufficient examples for learning.
- Exclusivity: The zip file should exclusively contain images and metadata.jsonl.
No additional files or nested folders should be included.
Some points to keep in mind:
- The images must be jpeg, jpg or png.
- There should be at least 5 images per split.
- There must not be any other files in the zip file.
- There must not be any other folders inside the zip folder.
When train.zip is decompressed, it creates no folders: only images and metadata.jsonl.
## Parameters
[[autodoc]] trainers.object_detection.params.ObjectDetectionParams
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